{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This notebook was used to download the output of runs \n",
    "# and then create a weighted ensemble using the  \n",
    "# optimal weights.\n",
    "from os.path import join\n",
    "\n",
    "import boto3\n",
    "from botocore.exceptions import ClientError\n",
    "\n",
    "from rastervision.common.settings import results_path\n",
    "from rastervision.common.utils import _makedirs\n",
    "\n",
    "test_pred_fn = 'test_preds.csv'\n",
    "scores_fn = 'scores.json'\n",
    "s3_bucket = 'raster-vision'\n",
    "out_path = '/opt/data/results/jupyter_out/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def s3_download(run_name, file_name, new_file_name=None):\n",
    "    if new_file_name is None:\n",
    "        new_file_name = file_name\n",
    "    s3_key = 'results/{}/{}'.format(run_name, file_name)\n",
    "    run_path = join('/opt/data/results/', run_name, new_file_name)\n",
    "    s3 = boto3.resource('s3')\n",
    "    s3.Bucket(s3_bucket).download_file(s3_key, run_path)\n",
    "    \n",
    "def download_run(run_name):\n",
    "    _makedirs(join('/opt/data/results/', run_name))\n",
    "    try:\n",
    "        s3_download(run_name, test_pred_fn)\n",
    "    except:\n",
    "        s3_download(run_name, 'test_predictions.csv', test_pred_fn)\n",
    "        \n",
    "    s3_download(run_name, scores_fn)\n",
    "    s3_download(run_name, 'validation_preds.csv')\n",
    "    s3_download(run_name, 'train_preds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tagging/7_5_17/ensemble/0\n",
      "tagging/7_5_17/ensemble/1\n",
      "tagging/7_5_17/ensemble/2\n",
      "tagging/7_5_17/ensemble/3\n",
      "tagging/7_5_17/ensemble/4\n",
      "tagging/7_18_17/80/resnet/0\n",
      "tagging/7_18_17/80/resnet/1\n",
      "tagging/7_18_17/80/resnet/2\n",
      "tagging/7_18_17/80/resnet/3\n",
      "tagging/7_18_17/80/resnet/4\n",
      "tagging/7_18_17/80/inception/0\n",
      "tagging/7_18_17/80/inception/1\n",
      "tagging/7_18_17/80/inception/2\n",
      "tagging/7_18_17/80/inception/3\n",
      "tagging/7_18_17/80/inception/4\n"
     ]
    }
   ],
   "source": [
    "# Use 80% of dev set\n",
    "run_names = [\n",
    "    'tagging/7_5_17/ensemble/0',\n",
    "    'tagging/7_5_17/ensemble/1',\n",
    "    'tagging/7_5_17/ensemble/2',\n",
    "    'tagging/7_5_17/ensemble/3',\n",
    "    'tagging/7_5_17/ensemble/4',\n",
    "    'tagging/7_18_17/80/resnet/0',\n",
    "    'tagging/7_18_17/80/resnet/1',\n",
    "    'tagging/7_18_17/80/resnet/2',\n",
    "    'tagging/7_18_17/80/resnet/3',\n",
    "    'tagging/7_18_17/80/resnet/4',\n",
    "    'tagging/7_18_17/80/inception/0',\n",
    "    'tagging/7_18_17/80/inception/1',\n",
    "    'tagging/7_18_17/80/inception/2',\n",
    "    'tagging/7_18_17/80/inception/3',\n",
    "    'tagging/7_18_17/80/inception/4'  \n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for run_name in run_names:\n",
    "    print(run_name)\n",
    "    download_run(run_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from os.path import splitext, basename\n",
    "import glob\n",
    "\n",
    "def generate_file_inds(path):\n",
    "    paths = sorted(\n",
    "        glob.glob(join(path, '*.{}'.format('jpg'))))\n",
    "\n",
    "    file_inds = []\n",
    "    for path in paths:\n",
    "        file_ind = splitext(basename(path))[0]\n",
    "        file_inds.append(file_ind)\n",
    "    return file_inds\n",
    "\n",
    "dev_file_inds = generate_file_inds('/opt/data/datasets/planet_kaggle/train-jpg')\n",
    "nb_train_inds = int(len(dev_file_inds) * 0.8)\n",
    "train_file_inds = dev_file_inds[0:nb_train_inds]\n",
    "val_file_inds = dev_file_inds[nb_train_inds:]\n",
    "test_file_inds = generate_file_inds('/opt/data/datasets/planet_kaggle/test-jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rastervision.tagging.data.planet_kaggle import TagStore\n",
    "\n",
    "results_path = '/opt/data/results/'\n",
    "train_tag_stores = []\n",
    "val_tag_stores = []\n",
    "for run_name in run_names:\n",
    "    val_path = join(results_path, run_name, 'validation_preds.csv')\n",
    "    val_tag_stores.append(TagStore(val_path))\n",
    "    \n",
    "    train_path = join(results_path, run_name, 'train_preds.csv')\n",
    "    train_tag_stores.append(TagStore(train_path))\n",
    "    \n",
    "gt_csv_path = '/opt/data/datasets/planet_kaggle/train_v2.csv'\n",
    "gt_tag_store = TagStore(gt_csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tagging/7_5_17/ensemble/0\n",
      "tagging/7_5_17/ensemble/1\n",
      "tagging/7_5_17/ensemble/2\n",
      "tagging/7_5_17/ensemble/3\n",
      "tagging/7_5_17/ensemble/4\n",
      "tagging/7_18_17/80/resnet/0\n",
      "tagging/7_18_17/80/resnet/1\n",
      "tagging/7_18_17/80/resnet/2\n",
      "tagging/7_18_17/80/resnet/3\n",
      "tagging/7_18_17/80/resnet/4\n",
      "tagging/7_18_17/80/inception/0\n",
      "tagging/7_18_17/80/inception/1\n",
      "tagging/7_18_17/80/inception/2\n",
      "tagging/7_18_17/80/inception/3\n",
      "tagging/7_18_17/80/inception/4\n",
      "(8096, 17, 15)\n",
      "(32383, 17, 15)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import fbeta_score\n",
    "\n",
    "val_preds = []\n",
    "train_preds = []\n",
    "for i, run_name in enumerate(run_names):\n",
    "    print(run_name)\n",
    "    run_val_preds = np.expand_dims(val_tag_stores[i].get_tag_array(val_file_inds), axis=2)\n",
    "    val_preds.append(run_val_preds)\n",
    "    \n",
    "    run_train_preds = np.expand_dims(train_tag_stores[i].get_tag_array(train_file_inds), axis=2)\n",
    "    train_preds.append(run_train_preds)\n",
    "    \n",
    "val_preds = np.concatenate(val_preds, axis=2)\n",
    "print(val_preds.shape)\n",
    "\n",
    "train_preds = np.concatenate(train_preds, axis=2)\n",
    "print(train_preds.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.93331 [8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8]\n",
      "0.93331 [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7]\n",
      "0.93331 [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n",
      "0.93331 [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9]\n",
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      "0.93331 [9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8]\n",
      "0.93331 [9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9]\n",
      "0.93331 [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8]\n",
      "0.93331 [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]\n",
      "0.93332 [8, 8, 8, 8, 8, 5, 5, 5, 5, 5, 7, 7, 7, 7, 7]\n",
      "0.93332 [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1]\n",
      "0.93332 [2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2]\n",
      "0.93332 [3, 3, 3, 3, 3, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3]\n",
      "0.93332 [4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 4, 4, 4, 4, 4]\n",
      "0.93332 [4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4]\n",
      "0.93332 [7, 7, 7, 7, 7, 9, 9, 9, 9, 9, 7, 7, 7, 7, 7]\n",
      "0.93332 [6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1]\n",
      "0.93332 [7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 1, 1, 1, 1, 1]\n",
      "0.93332 [8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 1, 1, 1, 1, 1]\n",
      "0.93332 [9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1]\n",
      "0.93332 [5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3]\n",
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      "0.93332 [7, 7, 7, 7, 7, 9, 9, 9, 9, 9, 2, 2, 2, 2, 2]\n",
      "0.93333 [7, 7, 7, 7, 7, 5, 5, 5, 5, 5, 2, 2, 2, 2, 2]\n",
      "0.93333 [6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4]\n",
      "0.93334 [7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2]\n",
      "0.93334 [8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 2, 2, 2, 2, 2]\n",
      "0.93334 [5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4]\n",
      "0.93334 [9, 9, 9, 9, 9, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7]\n",
      "0.93334 [7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 5, 5, 5, 5, 5]\n",
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      "0.93335 [9, 9, 9, 9, 9, 7, 7, 7, 7, 7, 6, 6, 6, 6, 6]\n",
      "0.93335 [6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 2, 2, 2, 2, 2]\n",
      "0.93335 [7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6]\n",
      "0.93336 [5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5]\n",
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      "0.93336 [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2]\n",
      "0.93336 [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 3, 3, 3, 3]\n",
      "0.93336 [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 3, 3, 3, 3, 3]\n",
      "0.93336 [7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 3, 3, 3, 3, 3]\n",
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      "0.93336 [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 5, 5, 5, 5, 5]\n",
      "0.93336 [9, 9, 9, 9, 9, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7]\n",
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      "0.93339 [8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 0, 0, 0, 0, 0]\n",
      "0.93339 [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0]\n",
      "0.93339 [8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 7, 7, 7, 7, 7]\n",
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      "0.93339 [7, 7, 7, 7, 7, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6]\n",
      "0.93340 [7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4]\n",
      "0.93340 [9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 5, 5, 5, 5, 5]\n",
      "0.93340 [4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2]\n",
      "0.93340 [8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4]\n",
      "0.93340 [9, 9, 9, 9, 9, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5]\n",
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      "0.93341 [9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6]\n",
      "0.93341 [9, 9, 9, 9, 9, 7, 7, 7, 7, 7, 5, 5, 5, 5, 5]\n",
      "0.93342 [8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7]\n",
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      "0.93342 [6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 7, 7, 7, 7, 7]\n",
      "0.93343 [9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7]\n",
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      "0.93343 [4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]\n",
      "0.93343 [6, 6, 6, 6, 6, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]\n",
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      "0.93343 [8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 4, 4, 4, 4, 4]\n",
      "0.93345 [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2]\n",
      "0.93345 [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4]\n",
      "0.93345 [9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 6, 6, 6, 6, 6]\n",
      "0.93346 [4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 3, 3, 3, 3, 3]\n",
      "0.93346 [9, 9, 9, 9, 9, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8]\n",
      "0.93347 [5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3]\n",
      "0.93349 [3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1]\n",
      "0.93349 [6, 6, 6, 6, 6, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2]\n",
      "0.93350 [9, 9, 9, 9, 9, 6, 6, 6, 6, 6, 8, 8, 8, 8, 8]\n",
      "0.93351 [5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 2, 2, 2, 2, 2]\n",
      "0.93352 [7, 7, 7, 7, 7, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5]\n",
      "0.93352 [7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 3, 3, 3, 3, 3]\n",
      "0.93353 [6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]\n",
      "0.93357 [7, 7, 7, 7, 7, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4]\n",
      "0.93359 [8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 3, 3, 3, 3, 3]\n",
      "0.93362 [8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 5, 5, 5, 5, 5]\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "def get_f2(w, is_train=True):\n",
    "    if is_train:\n",
    "        train_preds_sum = np.sum(train_preds * [[w]], axis=2)\n",
    "        weight_sum = np.sum(w)\n",
    "        true_thresh = math.ceil(weight_sum / 2)\n",
    "        our_train_preds = train_preds_sum >= true_thresh\n",
    "        gt_train_preds = gt_tag_store.get_tag_array(train_file_inds)\n",
    "        f2 = fbeta_score(\n",
    "            gt_train_preds, our_train_preds, beta=2, average='samples')\n",
    "    else:\n",
    "        val_preds_sum = np.sum(val_preds * [[w]], axis=2)\n",
    "        weight_sum = np.sum(w)\n",
    "        true_thresh = math.ceil(weight_sum / 2)\n",
    "        our_val_preds = val_preds_sum >= true_thresh\n",
    "        gt_val_preds = gt_tag_store.get_tag_array(val_file_inds)\n",
    "        f2 = fbeta_score(\n",
    "            gt_val_preds, our_val_preds, beta=2, average='samples')\n",
    "\n",
    "    \n",
    "    return f2\n",
    "\n",
    "import itertools\n",
    "possible_weights = list(range(0, 10))\n",
    "weights = []\n",
    "f2_scores = []\n",
    "for w in itertools.product(*[possible_weights]*3):\n",
    "    w = [w[0]] * 5 + [w[1]] * 5 + [w[2]] * 5\n",
    "    f2 = get_f2(w, is_train=False)\n",
    "    f2_scores.append(f2)\n",
    "    weights.append(w)\n",
    "    \n",
    "results = sorted(zip(f2_scores, weights), key=lambda x: x[0])\n",
    "for r in results:\n",
    "    print('{:.5f} {}'.format(r[0], r[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_test_preds(run_names, weights, out_path):\n",
    "    test_preds_list = []\n",
    "    for run_name in run_names:\n",
    "        run_path = join(results_path, run_name)\n",
    "        test_pred_path = join(run_path, test_pred_fn) \n",
    "        test_preds = TagStore(test_pred_path).get_tag_array(test_file_inds)\n",
    "        test_preds_list.append(np.expand_dims(test_preds, axis=2))\n",
    "    \n",
    "    test_preds = np.concatenate(test_preds_list, axis=2)\n",
    "    test_preds_sum = np.sum(test_preds * [[weights]], axis=2)\n",
    "    weight_sum = np.sum(weights)\n",
    "    true_thresh = math.ceil(weight_sum / 2)\n",
    "    our_test_preds = test_preds_sum >= true_thresh\n",
    "\n",
    "    tag_store = TagStore()\n",
    "    for i in range(our_test_preds.shape[0]):\n",
    "        tag_store.add_tags(test_file_inds[i], our_test_preds[i, :])\n",
    "\n",
    "    tag_store.save(out_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "best_weights = [8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 5, 5, 5, 5, 5]\n",
    "# Use models trained on 99% of dev set for final predictions\n",
    "run_names = [\n",
    "    'tagging/7_19_17/99/inception/0',\n",
    "    'tagging/7_19_17/99/inception/1',\n",
    "    'tagging/7_19_17/99/inception/2',\n",
    "    'tagging/7_19_17/99/inception/3',\n",
    "    'tagging/7_19_17/99/inception/4',\n",
    "    'tagging/7_16_17/resnet/0',\n",
    "    'tagging/7_16_17/resnet/1',\n",
    "    'tagging/7_16_17/resnet/2',\n",
    "    'tagging/7_16_17/resnet/3',\n",
    "    'tagging/7_16_17/resnet/4',\n",
    "    'tagging/7_16_17/densenet/0',\n",
    "    'tagging/7_16_17/densenet/1',\n",
    "    'tagging/7_16_17/densenet/2',\n",
    "    'tagging/7_16_17/densenet/3',\n",
    "    'tagging/7_16_17/densenet/4'\n",
    "]\n",
    "make_test_preds(run_names, best_weights, join(out_path, 'weighted_5rn_5dn_5in_99.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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